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" '''\n",
" Probability of recidivism (negative event) given personal properties (x)\n",
" and predictive model (model).\n",
" '''\n",
" if x.ndim == 1:\n",
" # if x is vector, transform to column matrix.\n",
" f_values = model.predict_proba(np.array(x).reshape(-1, 1))\n",
" else:\n",
" f_values = model.predict_proba(x)\n",
"\n",
" return f_values[:, 0]\n",
"\n",
"\n",
"def ep(r, df, result_col, feature_cols, model):\n",
" '''\n",
" Returns:\n",
" Empirical performance, i.e. percentage of recidivists. \n",
" \n",
" Parameters:\n",
" r = leniency rate\n",
" df = test data, pandas DataFrame\n",
" result_col = String (list), name of column containing the binarized results.\n",
" feature_cols = String (list), name of columns containge individual features.\n",
" model = trained sklearn classifier \n",
" '''\n",
" rates = np.zeros_like(r)\n",
" for i in range(len(rates)):\n",
" rates[i] = np.mean((df[result_col] == 0) &\n",
" (f(df[feature_cols], model) < r[i]))\n",
" return rates\n",
"def gp(r, df, feature_cols, y_model, x_model):\n",
" '''\n",
" Returns:\n",
" Generalized performance\n",
" \n",
" Parameters:\n",
" r = leniency rate\n",
" df = test data, pandas DataFrame\n",
" feature_cols = String (list), name of columns containing individual features.\n",
" y_model = trained sklearn classifier to predict response\n",
" x_model = model of P(X=x)\n",
" '''\n",
" preds = f(df[feature_cols], y_model)\n",
" \n",
" return np.sum(preds * (preds < r) * x_model(df[feature_cols]))"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Performance comparison\n",
"\n",
"Below we try to replicate the results obtained by Lakkaraju and compare their model's performance to the one of ours.\n",
"Lakkaraju says that they used logistic regression to predict recidivism. We models using only *observed observations*, i.e. defendants that were granted bail and are in the train set. We then predict the probability of recidivism for all observations in the test data and attach it to our data set. I also applied random forest classifier."
"metadata": {},
"outputs": [],
"source": [
"# instantiate the model (using the default parameters)\n",
"logreg = LogisticRegression(solver='lbfgs')\n",
"\n",
"# fit, reshape X to be of shape (n_samples, n_features)\n",
"logreg.fit(train_labeled.X.values.reshape(-1, 1), train_labeled.result_Y)\n",
"\n",
"# predict probabilities and attach to data\n",
"label_probs_logreg = logreg.predict_proba(test.X.values.reshape(-1, 1))\n",
"test = test.assign(B_prob_0_logreg=label_probs_logreg[:, 0])\n",
"# instantiate the model (using the default parameters)\n",
"forest = RandomForestClassifier(n_estimators=300, max_depth=5, random_state=0)\n",
"# fit, reshape X to be of shape (n_samples, n_features)\n",
"forest = forest.fit(train_labeled.X.values.reshape(-1, 1), train_labeled.result_Y)\n",
"\n",
"# predict probabilities and attach to data\n",
"label_probs_forest = forest.predict_proba(test.X.values.reshape(-1, 1))\n",
"test = test.assign(B_prob_0_forest=label_probs_forest[:, 0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's plot the failure rates against the acceptance rates using the difference."
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": false
},
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"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0.0179 0.0179 0. 0.0199 0.0183 0.0274]\n",
" [0.0312 0.0357 0.0022 0.0319 0.0568 0.0443]\n",
" [0.0625 0.067 0.0167 0.0677 0.0991 0.0928]\n",
" [0.1071 0.1071 0.0366 0.1116 0.1335 0.1335]\n",
" [0.1607 0.1473 0.1126 0.1633 0.1625 0.1576]\n",
" [0.2098 0.2098 0.207 0.2271 0.1854 0.188 ]\n",
" [0.2679 0.2679 0.2818 0.2749 0.1993 0.2011]\n",
" [0.3125 0.3214 0.3877 0.3546 0.2102 0.2131]]\n"
]
}
],
"failure_rates = np.zeros((8, 6))\n",
" ## Contraction, logistic regression\n",
" failure_rates[r - 1, 0] = contraction(\n",
" test[test.decision_T == 1], 'judgeID_J', 'decision_T', 'result_Y',\n",
" 'B_prob_0_logreg', 'acceptanceRate_R', r / 10, False)\n",
" \n",
" ## Contraction, random forest\n",
" failure_rates[r - 1, 1] = contraction(\n",
" test[test.decision_T == 1], 'judgeID_J', 'decision_T', 'result_Y',\n",
" 'B_prob_0_forest', 'acceptanceRate_R', r / 10, False)\n",
" ## Human error rate - Correct?\n",
" # Get judges with correct leniency as list\n",
" correct_leniency_list = test_labeled.judgeID_J[test_labeled['acceptanceRate_R'].round(1) ==\n",
" r / 10]\n",
"\n",
" # Released are the people they judged and released, T = 1\n",
" released = test_labeled[test_labeled.judgeID_J.isin(correct_leniency_list)]\n",
"\n",
" # Get their failure rate, aka ratio of reoffenders to number of people judged in total\n",
" failure_rates[r - 1, 2] = np.sum(\n",
" released.result_Y == 0) / correct_leniency_list.shape[0]\n",
"\n",
" ## True evaluation -- didn't mention using contraction here???\n",
" failure_rates[r - 1, 3] = contraction(test, 'judgeID_J', 'decision_T',\n",
" 'result_Y', 'B_prob_0_logreg',\n",
" 'acceptanceRate_R', r / 10, False)\n",
"\n",
" ## Causal model with logistic regression\n",
" failure_rates[r - 1, 4] = ep([r / 10], test_labeled, 'result_Y', 'X', logreg)\n",
" ## Causal model with random forest classifier\n",
" failure_rates[r - 1, 5] = ep([r / 10], test_labeled, 'result_Y', 'X', forest)\n",
"\n",
"# klassifikaatioille scipy.stats semin kautta error barit xerr ja yerr argumenttien kautta\n",
"\n",
"plt.figure(figsize=(14, 8))\n",
"plt.plot(np.arange(0.1, 0.9, .1), failure_rates[:, 0], label='Contraction, logistic')\n",
"#plt.plot(np.arange(0.1, 0.9, .1), failure_rates[:, 1], label='Contraction, forest')\n",
"plt.plot(np.arange(0.1, 0.9, .1), failure_rates[:, 2], label='\"Human judges\"')\n",
"plt.plot(np.arange(0.1, 0.9, .1), failure_rates[:, 3], label='True Evaluation')\n",
"\n",
"plt.plot(np.arange(0.1, 0.9, .1), failure_rates[:, 4], label='Causal model, log.')\n",
"#plt.plot(np.arange(0.1, 0.9, .1), failure_rates[:, 5], label='Causal model, r.f.')\n",
"plt.title('Failure rate vs. Acceptance rate')\n",
"plt.xlabel('Acceptance rate')\n",
"plt.ylabel('Failure rate')\n",
"plt.legend()\n",
"plt.show()\n",
"\n",
"with np.printoptions(precision=4, suppress=True):\n",
" print(failure_rates)"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Failure rates still too high. Order of curves now correct."
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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akJmvlTvLFWCkUwd11S9PH6jBPaKdngcAHoHgAQDAi1hrlZVXrvmZ+Xp33V7VNrrUJyFC90wZpAtH9VK36DCnJwKARyF4AADwAkWV9XpjVYEWZhZoe0mNIkICde7wnrpkTKJGJXfmMAIA+B4EDwAAHqqx2a2PNxZpfma+PttSLLeVxqbG6aaJfXX28B4KD+HLOAAcCX9TAgDgYXaU1Gj213l6a81uldU0qnt0mG6e2E8XjU5USnyE0/MAwKsQPAAAeAi32+qVr3fqDx9skrXSaWnddHFGok7sn6DAAJ6yBgDHguABAMADFFXW684Fa/XF1hKdMjBBj/90uLpyAAEA/GgEDwAADns/e6/ufzNb9U0u/fYnQ3XluGQOIQCANkLwAADgkKr6Jv1mcY7eWFWg4Ykx+p9LR6pvQqTTswDApxA8AAA4YMWOMt0xf432VNTp56f2062T+is4MMDpWQDgcwgeAAA60J6KOj2+ZJPeXrNHyXHhWnDjeI3uHef0LADwWQQPAAAdoK7RpWc/36ZnPtsmt5VuOaWfbprYVxGhfCkGgPbE37IAALQja60Wr92jxz7YpL3763X28B6678xBSuwc7vQ0APALBA8AAO1k9a5yPfpujlbtqtDQXtF66rJ0jU3l6WsA0JEIHgAA2tD+uiYtXrtHCzLzta5gvxKiQvXERcN10ahEBXDzUADocAQPAAA/kttt9dW2Us3PzNeHGwrV0OzWoO5ReuicNF0yJkmRvE4HABzD38AAAByj3RV1mr8yXwuzCrS7ok4xnYJ12ZgkXZyRpCE9o7l5KAB4AIIHAICj4HJb/WfzPs35Zpc+3bxPVtKEfvG698xBOi2tm8KCA52eCAA4CMEDAEArFO6v1+sr8/X6yl3as79eCVGhunliP106JklJcZy4BgCeiuABAOAH5Oyp1JNLt+jjTfvkclud2D9eD52bpkmDuyk4MMDpeQCAIyB4AAA4jPoml55culX/+mK7osOCdP2JfXT52CT17hLh9DQAwFEgeAAAOMSy3BLd/2a28kprdWlGku47a5Biw0OcngUAOAYEDwAALcprGvXb9zbqjVUFSo2P0Nzrj9P4vl2cngUA+BEIHgCA37PWavHaPXrknRztr2vSz07pq5+f2p8T1wDABxA8AAC/VtvYrLsWrNN72Xs1IilWr144TIN7RDs9CwDQRggeAIDf2lNRp+tfyVTO3krdPWWgbjiprwIDuFkoAPgSggcA4JdW7SrXzFey1NDk0ovXjNEpg7o6PQkA0A4IHgCA31m0qkD3vpGtHrFhmnv9OPXvFuX0JABAOyF4AAB+w+W2euLDTXr2s+0a36eL/nHlKHWO4LhpAPBlBA8AwC9U1Tfptnlr9PGmfbpyXLJ+c94QBQcGOD0LANDOCB4AgM9bV1Ch215fo7zSWj16/hBdPT7F6UkAgA5C8AAAfFazy61nPtumJ5duVUJUqF6dMY4biQKAnyF4AAA+aVdprW6fv0ZZeeU6d0RP/fb8oYoJD3Z6FgCggxE8AACfYq3VgswCPfzOBgUEGD112UidP7KX07MAAA4heAAAPqOsplH3LVqnDzcU6bg+cfrzJSPVK7aT07MAAA4ieAAAPmFpTpHuXZStyromPXDWYM2YkKqAAOP0LACAwwgeAIBXq6xv0iPv5GhhVoEGdY/SK9PHKq1ntNOzAAAeguABAHitL7eW6O6Fa1VU1aBbTumnWyf1V0gQ99YBAPw/ggcA4HVqGpr12AebNHt5nvomROiNm47XyKRYp2cBADwQwQMA8Cord5bpzgVrtausVtdNSNWdZwxUWHCg07MAAB6K4AEAeAWX2+qvH2/VXz/ZqqTO4Xp95niNTY1zehYAwMMRPAAAj1dUWa9fzFut5dvL9NNRiXrk/CGKCOVLGADgyPhqAQDwaP/ZvE93zF+rukaX/nTxCF00OtHpSQAAL0LwAAA8UpPLrT9/tEXPfLZNg7pH6e9XjFK/rpFOzwIAeBmCBwDgcXZX1Onnc1Zp1a4KXTEuWQ+dk8bBBACAY0LwAAA8yrvr9uiBN9cfOKTg8nSdN6Kn05MAAF6M4AEAeITymkY9+PZ6vbtur0Ykxuipy9KVEh/h9CwAgJcjeAAAjluaU6R7F2Vrf12j7jpjoG44qY+CAgOcngUA8AEEDwDAMZX1TXr0nRwtyCrQoO5RemX6WKX1jHZ6FgDAhxA8AABHfLm1RHcvXKvCynrdcko/3Tqpv0KCuKoDAGhbBA8AoEPVN7n0+JJNemnZTvVJiNAbNx2v9OTOTs8CAPgoggcA0GG2FlXp53NXa1NhlaYdn6J7zxzEcdMAgHZF8AAA2p21VnNW7NKj7+YoIiRIL00bo1MGdXV6FgDADxA8AIB2VV7TqHsXrdOHG4p0Yv94/fmSEeoaFeb0LACAnyB4AADt5uttpbr99TUqrWnQA2cN1owJqQoIME7PAgD4EYIHANDmahub9dTSrXrui+1K6RKhRVNP0LDEGKdnAQD8EMEDAGhT/84p0m8Wb9DuijpdPjZJvzo7TRGhfLkBADiDr0AAgDZRUF6r3yzO0dKNRRrQLVILbhyvMSlxTs8CAPg5ggcA8KM0udx68csdenLpVknSfWcO0vQJqQoO5CaiAADnETwAgGOWlVeu+xdla3NRlU5L66Zfn5umxM7hTs8CAOBbBA8A4JjM+WaXHnp7vbpFh+m5q0fr9CHdnZ4EAMB/IXgAAEel2eXWo+/maNbXeZo4MEF/vTxd0WHBTs8CAOCwCB4AQKvtr23Sz+as0pe5Jbr+xFTde+ZgBXJfHQCAB2vVK0qNMVOMMZuNMbnGmHsP8/6TjDGrjDHNxpiLDnmfyxizpuW/xW01HADQsXL3Vesn/1imb3aU6omLhuuBs9OIHQCAxzviFR5jTKCkpyWdJqlA0kpjzGJrbc5BD9slaZqkOw/zKeqstSPbYCsAwCGfbSnWLXNWKSQwQHOvP04ZHDcNAPASrXlK21hJudba7ZJkjJkn6XxJ3waPtXZny/vc7bARAOAQa61eXLZTv3svRwO7R+tfU0dzChsAwKu0Jnh6Sco/6O0CSeOO4vcIM8ZkSmqW9Ji19q1DH2CMmSlppiQlJycfxacGALSX/XVNumfhOi3ZUKgzhnTTXy4ZqYhQXvoJAPAurfnKdbgnaNuj+D2SrbV7jDF9JH1ijMm21m77ziez9jlJz0lSRkbG0XxuAEA7WL2rXD+fu1qF++v1wFmDNWNCqgJ4vQ4AwAu1JngKJCUd9HaipD2t/Q2stXtaftxujPmPpHRJ237wgwAAjrDW6vkvdujxJZvULTpMC24cr/Tkzk7PAgDgmLUmeFZK6m+MSZW0W9Jlkq5ozSc3xnSWVGutbTDGxEs6QdITxzoWANB+ymsadeeCtfp40z6dMaSbnvjpCMWEc38dAIB3O2LwWGubjTG3SPpQUqCkF621G4wxj0jKtNYuNsaMkfSmpM6SzjXGPGytHSJpsKRnWw4zCNCB1/DkfM9vBQBwyMqdZbp17mqVVjfq4fOGaOr43jKGp7ABALyfsdazXjKTkZFhMzMznZ4BAH6hoLxWTy7dqkWrCpQUF66nrxilob1inJ4FAMARGWOyrLUZR3ocx+0AgB8qqW7Q05/m6rXluyQjTT8hVb+Y3F9RYTyFDQDgWwgeAPAjVfVNev6LHXr+i+2qa3Lpkowk3Tqpv3rGdnJ6GgAA7YLgAQA/0Oxya9bXeXr601yV1TTqrGHddcdpA9Wva6TT0wAAaFcEDwD4OGutHlq8QXO+2aUJ/eJ11xkDNSIp1ulZAAB0CIIHAHzc7OV5mvPNLt1wch/dd+Zgp+cAANChApweAABoP8tyS/TwOzmaPLir7j5jkNNzAADocAQPAPioHSU1uvm1VeqbEKEnL0tXYAD31QEA+B+CBwB8UGV9k66btVIBRnp+6hhFhvIMZgCAf+IrIAD4GJfb6udzViuvtFavXjdOyV3CnZ4EAIBjCB4A8DF/eH+jPttSrN9fMEzH9eni9BwAABzFU9oAwIfMX5mv57/coWnHp+iKcclOzwEAwHEEDwD4iI82FOqBt7I1oV+8fnU2x08DACDxlDYA8Hp799fp4cU5WrKhUIO6R+npK0YpKJB/zwIAQCJ4AMBrNbvcevmrnfqff2+Ry1rdPWWgrpvQRyFBxA4AAP+H4AEAL5SVV65fvbVeG/dW6tRBXfXweUOUFMdpbAAAHIrgAQAvUlHbqMeXbNbcFbvUIyZMz1w1WmcM6SZjuKkoAACHQ/AAgJdYv3u/bpidpcLKel1/YqpumzxAEdxQFACAH8RXSgDwAm+v2a173linzuEhWnTT8RqRFOv0JAAAvALBAwAezOW2emLJJj37+XaNSemsf1w5WglRoU7PAgDAaxA8AOChKmob9fO5q/XF1hJdfVxvPXhOGiewAQBwlAgeAPBAmwurNHN2pvZU1OmxC4fpsrHJTk8CAMArETwA4GGWrC/UHfPXKCI0SPNmHqfRveOcngQAgNcieADAQ1hr9c/PtumJJZs1IilWz141Wt1jwpyeBQCAVyN4AMADNDa79cCb2VqQVaBzR/TUHy8arrDgQKdnAQDg9QgeAHBYeU2jbnw1S9/sKNOtk/rr9sn9uZEoAABthOABAAftKKnR9JdXand5nZ68dKR+kt7L6UkAAPgUggcAHPL1tlLd+GqWAgOMXrt+nMakcDgBAABtjeABAAfMz8zXA29mKzkuXC9NG6vkLuFOTwIAwCcRPADQgaobmvWbxRu0MKtAJ/Tron9cOVoxnYKdngUAgM8ieACgg6wrqNCtc1drV1mtbj21n26d1F9BgQFOzwIAwKcRPADQztxuq+e+2K4/fbhZCVGhmnv9cRrXp4vTswAA8AsEDwC0o32V9bpj/lp9mVuiKUO667GfDlNseIjTswAA8BsEDwC0k483FumuhetU29isP1w4TJeNSeL+OgAAdDCCBwDamNtt9eTSLfrrJ7lK6xGtv16ern5dI52eBQCAXyJ4AKAN1TY2647X12rJhkJdkpGoR38yVKFBgU7PAgDAbxE8ANBGdlfU6bpZmdpcWKkHz0nT9BNSeAobAAAOI3gAoA1k5ZXphtlZamhy68VpYzRxYFenJwEAABE8APCjLcwq0P2LstUzNkzzZmaoX9copycBAIAWBA8AHCOX2+rxJZv03OfbdUK/Lnr6ilEcOQ0AgIcheADgGOwoqdGdC9YqK69cU8f31oPnpCk4MMDpWQAA4BAEDwAcBbfbavbyPP3hg40KCQzQk5eO1E/Sezk9CwAAfA+CBwBaqaC8VncvXKevtpVq4sAEPXbhcHWPCXN6FgAA+AEEDwAcgbVWCzIL9Mi7ObLW6rELh+nSMUkcOQ0AgBcgeADgB+yrrNe9i7L1yaZ9Oq5PnP540QglxYU7PQsAALQSwQMA3+PjjUW6a+E61TQ069fnpuma8SkKCOCqDgAA3oTgAYBD1De59NgHm/TyVzs1uEe0/nb5SO6tAwCAlyJ4AOAgW4qqdOvc1dpUWKXpJ6Tq7ikDFRYc6PQsAABwjAgeANCBgwle/WaXfvtujiJDg/TStDE6ZVBXp2cBAIAfieAB4PfKaxp1zxvr9FFOkU7sH68/XzJCXaM4bhoAAF9A8ADwW80ut+auzNdfPtqs6oZm/erswZp+QioHEwAA4EMIHgB+6cutJXr03RxtLqrSuNQ4/ea8IRrcI9rpWQAAoI0RPAD8yo6SGv3uvY1aurFISXGd9MxVo3TGkO7cRBQAAB9F8ADwC5X1Tfr7J7l6adkOhQQG6O4pAzX9hFROYAMAwMcRPAB8XkF5rS59drn27K/TxaMTdecZAzmUAAAAP0HwAPBphfvrdcW/vlFVfZMW3ni8Rvfu7PQkAADQgQgeAD6rpLpBVz6/XGU1jXr1unEamRTr9CQAANDBApweAADtoaK2UVc9/412V9TpxWljiB0AAPwUV3gA+JzK+iZNfXGFtpfU6MVrxmhsapzTkwAAgEO4wgPAp9Q0NOval1YqZ0+l/nnlKE3oH+/0JAAA4CCCB4DPqG9y6bpZmVq9q1x/vTxdkwZ3c3oSAABwGE9pA+ATGpvduvHVLC3fUaq/XDJCZw3r4fQkAADgAbjCA8DERFz1AAAgAElEQVTrud1Wdy1cq/9sLtbvLximC9ITnZ4EAAA8BMEDwKtZa/Xb9zbq7TV7dNcZA3X52GSnJwEAAA9C8ADwas9+vl0vLtuhacen6OaJfZ2eAwAAPAzBA8BrLcjM12MfbNI5w3vooXPSZIxxehIAAPAwBA8Ar/TJpiLduyhbE/rF68+XjFBAALEDAAD+G8EDwOtk5ZXr5tdWKa1HtJ65erRCgwKdngQAADwUwQPAq+Tuq9KMWSvVPTpML107RpGhnK4PAAC+H8EDwGsUVdZr6gsrFBQQoFemj1N8ZKjTkwAAgIcjeAB4hcZmt25+bZXKa5v08rVjlNwl3OlJAADAC/BcEABe4Xfv5Sgrr1x/uzxdQ3vFOD0HAAB4Ca7wAPB4b64u0Kyv8zRjQqrOHdHT6TkAAMCLEDwAPFrOnkrdtyhbY1PjdO+Zg5yeAwAAvAzBA8Bj7a9t0o2vZimmU7CevmKUggP5KwsAABwdXsMDwCO53Va3vb5ae/fXad7M8UqI4kQ2AABw9PjnUgAe6amPt+rTzcV66Jw0je7d2ek5AADASxE8ADzOJ5uK9NTHW3XhqF666rjeTs8BAABejOAB4FF2ltTotnlrlNYjWr+/YJiMMU5PAgAAXozgAeAxiqsaNPXFFQoIMHr26tEKCw50ehIAAPByBA8Aj1BV36RpL61QcVWDXpo2Rklx4U5PAgAAPoBT2gA4rqHZpRtmZ2lTYZWevyZD6ckcUgAAANoGV3gAOMrltrrj9bX6alupnvjpcJ0ysKvTkwAAgA9pVfAYY6YYYzYbY3KNMfce5v0nGWNWGWOajTEXHfK+a4wxW1v+u6athgPwftZaPfzOBr2XvVf3nzVIPx2d6PQkAADgY44YPMaYQElPSzpTUpqky40xaYc8bJekaZLmHPKxcZJ+LWmcpLGSfm2M4bkqACRJT3+aq1e+ztP1J6Zq5kl9nZ4DAAB8UGuu8IyVlGut3W6tbZQ0T9L5Bz/AWrvTWrtOkvuQjz1D0r+ttWXW2nJJ/5Y0pQ12A/By81bs0p8+2qIL0nvpvjMHOz0HAAD4qNYETy9J+Qe9XdDya63Rqo81xsw0xmQaYzKLi4tb+akBeKsl6wt1/5vZmjgwQU9cNFwBAdxrBwAAtI/WBM/hvhOxrfz8rfpYa+1z1toMa21GQkJCKz81AG/00YZC3TJnlUYkxeofV45ScCBnpwAAgPbTmu80CiQlHfR2oqQ9rfz8P+ZjAfiYDzcU6ubXVmlorxjNmj5W4SGcjA8AANpXa4JnpaT+xphUY0yIpMskLW7l5/9Q0unGmM4thxWc3vJrAPzMhxsK9bPXVmlYYoxemTFW0WHBTk8CAAB+4IjBY61tlnSLDoTKRknzrbUbjDGPGGPOkyRjzBhjTIGkiyU9a4zZ0PKxZZIe1YFoWinpkZZfA+BHlqz//9iZNZ3YAQAAHcdY29qX43SMjIwMm5mZ6fQMAG1kyfq9umXO6gNXdqaPVRSxAwAA2oAxJstam3Gkx/FqYQDthtgBAABOI3gAtIsPsg/EznBiBwAAOIjgAdDmXl2ep5+1HD09i9gBAAAO4kxYAG3GWqs/f7RFf/80V5MGddXfrkjn6GkAAOAovhMB0CaaXG7dtyhbC7MKdPnYJD16/lAFcVNRAADgMIIHwI9W09Csm15bpc+3FOv2yQN066R+MsY4PQsAAIDgAfDjFFc1aPrLK5Wzt1KP/3SYLh2T7PQkAACAbxE8AI7ZjpIaXfPiChVXNehfU0fr1EHdnJ4EAADwHQQPgGOSlVeu6185cJPguTOP08ikWIcXAQAA/DeCB8BR+yB7r257fY16xITppWvHKjU+wulJAAAAh0XwAGg1a61e+HKHfvf+RqUnxer5a8YoLiLE6VkAAADfi+AB0Cout9XD72zQK1/n6axh3fWXS0YqLDjQ6VkAAAA/iOABcES1jc26de5qLd24TzNP6qN7pwxSQADHTgMAAM9H8AD4Qfuq6nXdrEyt371fj54/RFePT3F6EgAAQKsRPAC+176qel34j69UWt2o567O0OQ0jp0GAADeheABcFgut9Uv5q5RSXWD5s0cz7HTAADAKxE8AA7ryaVb9PX2Uv3xouHEDgAA8FoBTg8A4Hn+s3mf/vZJri7JSNTFGUlOzwEAADhmBA+A79hTUafbX1+jQd2j9PB5Q52eAwAA8KMQPAC+1eRy65Y5q9TY7NY/rhylTiHcZwcAAHg3XsMD4FuPf7BJq3ZV6O9XpKtPQqTTcwAAAH40rvAAkCQtWV+o57/coWvG99Y5w3s6PQcAAKBNEDwAlFdao7sWrtWIxBjdf/Zgp+cAAAC0GYIH8HP1TS79bM4qGUl/v2KUQoN43Q4AAPAdvIYH8GONzW7d/Noqrd9dqeenZigpLtzpSQAAAG2KKzyAn2p2uXXb66v1yaZ9+t0FQzU5rZvTkwAAANocwQP4IZfb6q6F6/R+dqEePCdNV47r7fQkAACAdkHwAH7GWqsH3szWm6t3664zBmrGhFSnJwEAALQbggfwI9ZaPfxOjuatzNctp/TTz07p5/QkAACAdkXwAH7CWqvHl2zWy1/t1IwJqfrl6QOcngQAANDuCB7AT/ztk1w989k2XTkuWb86e7CMMU5PAgAAaHcED+AHnvlsm/7y7y366ahEPXr+UGIHAAD4DYIH8HHPf7Fdj32wSecM76EnLhqugABiBwAA+A+CB/BhLy3bod++t1FnDeuuJy8dqUBiBwAA+BmCB/BRs5fn6eF3cnR6Wjc9dVm6ggL5vzsAAPA/fAcE+KC5K3bpwbfWa9Kgrvr7FaMUTOwAAAA/xXdBgI+Zn5mv+9/M1sSBCfrHVaMUEsT/zQEAgP/iOyHAhyxaVaB73linCf3i9cxVoxUaFOj0JAAAAEcRPICPeHvNbt25YK2OS+2i567OUFgwsQMAAEDwAD7gvXV7dcf8tcpIidML0zLUKYTYAQAAkAgewOstWV+oW+etVnpSrF6aNkbhIUFOTwIAAPAYBA/gxZbmFOnnc1dpeGKMXrp2jCJCiR0AAICDETyAl/p08z7d/NoqpfWI1qzpYxUVFuz0JAAAAI9D8ABe6PMtxbphdpYGdI/UK9PHKZrYAQAAOCyCB/Ayy3JLdP0rmeqbEKlXZ4xTTDixAwAA8H0IHsCLLN9eqhmzVio1PkKvXTdOseEhTk8CAADwaAQP4CXWFVRoxssrldg5XK9eN05xEcQOAADAkRA8gBfI3VetaS+tVFxkiF67bpziI0OdngQAAOAVCB7Aw+2uqNPVL3yjAGM0e/o4dYsOc3oSAACA1yB4AA9WWt2gq1/4RtUNzXpl+lilxEc4PQkAAMCrEDyAh6qqb9K0l1Zqd3mdXrhmjNJ6Rjs9CQAAwOtwW3bAA9U3uTTzlSzl7K3Uv6aO1tjUOKcnAQAAeCWu8AAeptnl1q1zV+vr7aX608XDdeqgbk5PAgAA8FoED+BBrLW6/81sfZRTpF+fm6YL0hOdngQAAODVCB7Ag7zw5Q7NzyzQz0/tp2tPSHV6DgAAgNcjeAAP8VVuif7wwSadMaSbbp88wOk5AAAAPoHgATxAQXmtbpm7WqnxEfrzJSMVEGCcngQAAOATCB7AYfVNLt0wO0tNzW49d/VoRYZyeCIAAEBb4TsrwEHWWt23KFs5eyv1/NQM9UmIdHoSAACAT+EKD+Cgl5bt1Jurd+v2yQM0aTDHTwMAALQ1ggdwyNfbSvW79zfq9LRuuuWUfk7PAQAA8EkED+CA3RV1+tmcVUrpEq4/XzKCQwoAAADaCcEDdLD6JpdunJ2lxma3npuaoaiwYKcnAQAA+CwOLQA6kNttdfvra7R+z349d3WG+nJIAQAAQLviCg/QgZ74cLM+WF+oB84arNPSOKQAAACgvRE8QAeZt2KXnvlsm64cl6wZE1KdngMAAOAXCB6gA3y5tUQPvLVeJw9I0MPnDZExHFIAAADQEQgeoJ1tLarSTa9lqX/XSP39inQFBfJ/OwAAgI7Cd15AOyquatC1L69UWHCgXpg2hhPZAAAAOhjBA7ST+iaXrn8lUyXVDXrhmgz1iu3k9CQAAAC/w7HUQDtwu63umL9Gawsq9MxVozU8MdbpSQAAAH6JKzxAO3j8w016P/vA8dNnDOnu9BwAAAC/RfAAbWz28jw9+9l2XXUcx08DAAA4jeAB2tDHG4v067fXa9KgrvrNuRw/DQAA4DSCB2gj6woqdMuc1RrSM0Z/4/hpAAAAj8B3ZEAbyC+r1fSXMxUXEaIXpmUoPITzQAAAADwB35UBP9L+2iZd+/JKNTa7NG/mOHWNCnN6EgAAAFoQPMCP0NDs0szZmdpVWqtXZoxVv65RTk8CAADAQQge4Bi53VZ3L1ynb3aU6anLRuq4Pl2cngQAAIBD8Boe4Bj95d9b9PaaPbrrjIE6f2Qvp+cAAADgMAge4BjMz8zX3z/N1WVjknTzxL5OzwEAAMD3IHiAo/TVthLdvyhbE/rF69GfDOVeOwAAAB6sVcFjjJlijNlsjMk1xtx7mPeHGmNeb3n/N8aYlJZfTzHG1Blj1rT890zbzgc6Vu6+at04O0up8RF6+spRCuZeOwAAAB7tiIcWGGMCJT0t6TRJBZJWGmMWW2tzDnrYDEnl1tp+xpjLJD0u6dKW922z1o5s491AhyutbtD0l1cqJChAL04bo5hOwU5PAgAAwBG05p+nx0rKtdZut9Y2Spon6fxDHnO+pFktP18oaZLheT7wIfVNLs2cnaWiynr9a2qGkuLCnZ4EAACAVmhN8PSSlH/Q2wUtv3bYx1hrmyXtl/R/Z/SmGmNWG2M+M8ac+CP3Ah3O7ba6a+E6ZeWV638uHan05M5OTwIAAEArteY+PIe7UmNb+Zi9kpKttaXGmNGS3jLGDLHWVn7ng42ZKWmmJCUnJ7diEtBx/mfpFr2zdo/umTJIZw3r4fQcAAAAHIXWXOEpkJR00NuJkvZ832OMMUGSYiSVWWsbrLWlkmStzZK0TdKAQ38Da+1z1toMa21GQkLC0f8pgHYyPzNff/skV5dmJOnGk/s4PQcAAABHqTXBs1JSf2NMqjEmRNJlkhYf8pjFkq5p+flFkj6x1lpjTELLoQcyxvSR1F/S9raZDrSvt9fs1r1vrNOJ/eP12ws4fhoAAMAbHfEpbdbaZmPMLZI+lBQo6UVr7QZjzCOSMq21iyW9IGm2MSZXUpkORJEknSTpEWNMsySXpButtWXt8QcB2tJ76/bq9tfXaExKnJ67OoPjpwEAALyUsfbQl+M4KyMjw2ZmZjo9A35syfq9+tmc1RqVHKuXrx2riNDWvNQNAAAAHckYk2WtzTjS4/hna+Ag/84p0i1zVmtEYoxeInYAAAC8HsEDtPh00z7d/FqWhvSK0cvTxyqS2AEAAPB6BA8g6bMtxbrh1SwN6h6tV6aPVXRYsNOTAAAA0AYIHvi9ZbklmvlKpvolRGr2jLGK6UTsAAAA+AqCB37t441Fmv7ySqV0idCr141TbHiI05MAAADQhgge+K23Vu/WzNlZGtg9SnNnHqe4CGIHAADA1/CqbPil2V/v1EOLN2hcapz+NTVDUbxmBwAAwCcRPPAr1lr94z/b9McPN2vy4K76+xWjFBYc6PQsAAAAtBOCB37DWqs/fLBJz32+XRek99ITFw1XcCDP6gQAAPBlBA/8gsttdf+ibL2ema+p43vrN+cOUUCAcXoWAAAA2hnBA5/X5HLrtnlr9F72Xv381H6647QBMobYAQAA8AcED3xa80Gx88BZg3X9SX2cngQAAIAORPDAZ7ncVnfMX6v3svfqV2cP1nUnEjsAAAD+hldswye53FZ3LVirxWv36J4pg4gdAAAAP0XwwOe43Vb3vLFOi1bv1p2nD9BNE/s6PQkAAAAOIXjgU9xuqwfeytbCrAL9YlJ/3XJqf6cnAQAAwEEED3yGtVYPLV6vuSvy9bNT+uq2ycQOAACAvyN44BOstXr4nRy9unyXbjipj+48fSBHTwMAAIBT2uD96ptcuveNdXprzR5NPyFV9545iNgBAACAJIIHXq60ukEzZ2cpK69cd54+QD87pR+xAwAAgG8RPPBaW4uqNH3WSu2rbNDfr0jXOcN7Oj0JAAAAHobggVf6cmuJbnotS6FBgZo38zilJ3d2ehIAAAA8EMEDr/PaN3l66O0N6t81Us9fk6HEzuFOTwIAAICHInjgNVxuqz+8v1HPf7lDEwcm6G+XpysqLNjpWQAAAPBgBA+8grVWD769XnO+2aVpx6foV2cPVlAgp6oDAADghxE88ApPf5qrOd/s0g0n99F9Zw52eg4AAAC8BP9EDo+3IDNff/poiy5I76V7zhjk9BwAAAB4EYIHHu2zLcW6b1G2JvSL1+M/Ha6AAO6xAwAAgNYjeOCxsgv266ZXs9S/W5T+edUohQTxP1cAAAAcHb6DhEfKL6vVtS+vVOfwEL187RhOYwMAAMAx4dACeJyymkZd8+IKNbncmjdznLpFhzk9CQAAAF6KKzzwKHWNLs2YtVK7K+r0wjUZ6tc1yulJAAAA8GJc4YHH2F/bpJmzM7Umv0L/vHK0MlLinJ4EAAAAL0fwwCPkl9Vq2ksrlF9WpycvHakpQ7s7PQkAAAA+gOCB49bmV2jGrJVqclnNnjFW4/p0cXoSAAAAfATBA0d9tKFQt85brYSoUM2bNlb9ukY6PQkAAAA+hOCBY15etkMPv5uj4Ymxen5qhhKiQp2eBAAAAB9D8KDDudxWv3tvo15ctkOnpXXTXy9LV6eQQKdnAQAAwAcRPOhQdY0u3f76Gi3ZUKhpx6fowXPSFBhgnJ4FAAAAH0XwoMOUVDfoulmZWltQoQfPSdOMCalOTwIAAICPI3jQIbYVV+val1aqqLJe/7xyNMdOAwAAoEMQPGh3K3aUaebsTAUao3kzj1N6cmenJwEAAMBPEDxoV++s3aNfzl+rxLhOennaWCV3CXd6EgAAAPwIwYN2Ya3VM59t1+NLNmlsSpyemzpaseEhTs8CAACAnyF40ObcbquHFq/Xq8t36dwRPfXHi4YrLJhjpwEAANDxCB60Kbfb6r5F2Xo9M183nNRH90wZpACOnQYAAIBDCB60Gbfb6t5F6zQ/s0C3ntpPt582QMYQOwAAAHAOwYM28Z3YmdRft0/uT+wAAADAcQQPfjS32+qeN9ZpQVaBfjGpv24/bYDTkwAAAABJUoDTA+DdiB0AAAB4MoIHx8zltrqb2AEAAIAH4yltOCbNLrfueSNbb6wq0G2T++u2ycQOAAAAPA/Bg6NW09CsW+as0qebi3X75AH6xeT+Tk8CAAAADovgwVEpqqzX9JdXalNhlX5/wTBdMS7Z6UkAAADA9yJ40GqbC6t07UsrVFHXpOevydApA7s6PQkAAAD4QQQPWmVZbolunJ2lTiGBmn/DeA3tFeP0JAAAAOCICB4c0cKsAt37xjr1TYjUi9eOUa/YTk5PAgAAAFqF4MH3stbqyaVb9dTHWzWhX7z+cdUoRYcFOz0LAAAAaDWCB4dVVd+kuxas05INhbpodKJ+f8EwhQRx2yYAAAB4F4IH/yV3X5VumJ2lnaW1euCswbruxFQZY5yeBQAAABw1ggff8UH2Xt25YK3CggP16oxxGt+3i9OTAAAAgGNG8ECS1Oxy648fbtazn2/XyKRY/fOqUeoRw+EEAAAA8G4ED1RS3aBb567WV9tKdeW4ZD10bppCgwKdngUAAAD8aASPn8vKK9ctc1aprKZRf7xouC7OSHJ6EgAAANBmCB4/5XJbPf1prp76eKt6xobpjZuO52aiAAAA8DkEjx/aU1Gn215foxU7ynT+yJ569CdDub8OAAAAfBLB42eWrN+re97IVrPLrT9fPEIXjurFkdMAAADwWQSPn6hrdOmRd3M0d8UujUiM0VOXpSslPsLpWQAAAEC7Inj8wPrd+3Xb62u0rbhaN03sq9snD1BIUIDTswAAAIB2R/D4sKr6Jv35oy165eudio8M1aszxumEfvFOzwIAAAA6DMHjg6y1ei97rx55J0fF1Q26+rje+uXpAxXTiYMJAAAA4F8IHh+zs6RGD769Xl9sLdHQXtH619QMjUiKdXoWAAAA4AiCx0c0NLv0zH+26+n/5CokMEC/OTdNV49PUWAAJ7ABAADAfxE8Xq7Z5dai1bv114+3qqC8TucM76EHz0lTt+gwp6cBAAAAjiN4vJTLbfX2mgOhs7O0VsMTY/SHC4fpxP4JTk8DAAAAPAbB42Xcbqt3s/fqqaVbtK24RoN7HHidzuTBXbmBKAAAAHAIgsdLWGv14YYi/eXfm7WlqFoDukXqn1eO0hlDuiuA1+kAAAAAh0XweIH1u/frkXdztGJHmfokROivl6frnGE9CB0A+N/27i3GrrKMw/jzdgpF2nKQKa30QNswI9AGRMeKIVFIUQrG1guUkhBBG7kRiIeYSDCRoBciIUYDHlAQIVFALnRilCoHgyEUGUNCaKUythUapAwtljKlh2lfL/YKGYa2sxhm9l579fklk6y917dn/pO8WXu/833rG0mSRmHDU2EDO3Zz85/Xc2/fCxx/9JF89zOLWfnhuUzumNTqaJIkSVJbsOGpoN1D+/jlY5u45eF+du3dx6pzFnD10i7/cagkSZL0DtnwVMieof38Zd0WbnzgWZ7ftpOlp57IdZ86jYUzprU6miRJktSWbHhabOeeIR791wAPPPMSDz37Mjt2DdF14jTu+uISPtbtFtOSJEnSu2HD0wLbd+7lwX9uYfXal3j0uQF27d3PcUcfwbJFs7hg0SzOff8M79ORJEmSxkGphicilgE/BDqAX2Tm90acnwLcBXwI2ApckpmbinPXAquAfcA1mbl63NJX3J6h/WzaOsj6l3bw3JYdrN+yg+e2vM6mrYPsT5h1zFFc0jOXCxbPYsn899rkSJIkSeNs1IYnIjqAW4FPAJuBJyOiNzPXDRu2Cng1M0+JiJXAjcAlEXE6sBJYBJwEPBgR3Zm5b7x/kVbJTLYN7mHjK4Nv+fr3wOtsGBhkaH8CMClgfudUumdO59NnnsR5p57IGbOPdWtpSZIkaQKVmeFZAvRn5gaAiLgHWAEMb3hWANcXx/cDt0REFM/fk5m7gY0R0V98v8fHJ/7Eur53LRteGTzo+e1v7GXjwOu8tmvozecmTwrmnXA0Czuncv5pM+meOZ3umdNZOGMqRx3R0YzYkiRJkgplGp7ZwAvDHm8GPnKwMZk5FBHbgROK59eMeO3skT8gIq4ErgSYN29e2ewTbnD3EK+9sfeg56dN6WD5B05iQec0FnZOZUHnVOYc/x6XpkmSJEkVUabhOdCaqyw5psxryczbgNsAenp63na+VW767JmtjiBJkiTpXSgzFbEZmDvs8RzgxYONiYjJwLHAtpKvlSRJkqQJUabheRLoiogFEXEkjU0IekeM6QUuL44vBh7OzCyeXxkRUyJiAdAF/H18okuSJEnSoY26pK24J+cqYDWNbanvyMy1EXED0JeZvcDtwN3FpgTbaDRFFOPuo7HBwRDw5Trt0CZJkiSp2qIxEVMdPT092dfX1+oYkiRJkiosIv6RmT2jjXM7MUmSJEm1ZcMjSZIkqbZseCRJkiTVlg2PJEmSpNqy4ZEkSZJUWzY8kiRJkmrLhkeSJElSbdnwSJIkSaotGx5JkiRJtWXDI0mSJKm2bHgkSZIk1ZYNjyRJkqTasuGRJEmSVFs2PJIkSZJqKzKz1RneIiIGgP+0OscwncArrQ6htmPdaCysG42FdaOxsG40FlWrm5Mzc8ZogyrX8FRNRPRlZk+rc6i9WDcaC+tGY2HdaCysG41Fu9aNS9okSZIk1ZYNjyRJkqTasuEZ3W2tDqC2ZN1oLKwbjYV1o7GwbjQWbVk33sMjSZIkqbac4ZEkSZJUWzY8kiRJkmrLhqcQEcsiYn1E9EfENw9wfkpE3FucfyIi5jc/paqmRN18LSLWRcTTEfFQRJzcipyqltHqZti4iyMiI6LttgDV+CtTNxHxueKaszYift3sjKqeEu9T8yLikYh4qnivuqgVOVUdEXFHRLwcEc8c5HxExI+Kmno6Ij7Y7IzvlA0PEBEdwK3AhcDpwKURcfqIYauAVzPzFOAHwI3NTamqKVk3TwE9mXkGcD/w/eamVNWUrBsiYjpwDfBEcxOqisrUTUR0AdcC52TmIuArTQ+qSil5vfkWcF9mngWsBH7c3JSqoDuBZYc4fyHQVXxdCfykCZneFRuehiVAf2ZuyMw9wD3AihFjVgC/Ko7vB5ZGRDQxo6pn1LrJzEcyc2fxcA0wp8kZVT1lrjcA36HRIO9qZjhVVpm6+RJwa2a+CpCZLzc5o6qnTN0kcExxfCzwYhPzqYIy81Fg2yGGrADuyoY1wHER8b7mpBsbG56G2cALwx5vLp474JjMHAK2Ayc0JZ2qqkzdDLcK+NOEJlI7GLVuIuIsYG5m/qGZwVRpZa433UB3RDwWEWsi4lB/odXhoUzdXA9cFhGbgT8CVzcnmtrYO/3803KTWx2gIg40UzNyv+4yY3R4KV0TEXEZ0AN8fEITqR0csm4iYhKNZbNXNCuQ2kKZ681kGktMzqUxm/y3iFicmf+b4GyqrjJ1cylwZ2beHBEfBe4u6mb/xMdTm2q7z8TO8DRsBuYOezyHt0/pvjkmIibTmPY91HSf6q9M3RAR5wPXAcszc3eTsqm6Rqub6cBi4K8RsQk4G+h144LDXtn3qd9n5t7M3Aisp9EA6fBVpm5WAfcBZObjwFFAZ1PSqV2V+vxTJTY8DU8CXRGxICKOpHHTXu+IMb3A5cXxxcDD6X9tPdyNWjfF0qSf0Wh2XE8vGKVuMnN7ZnZm5vzMnE/j3q/lmdnXmriqiDLvU78DzgOIiE4aS9w2NDWlqqZM3TwPLAWIiNNoNDwDTU2pdtMLfL7Yre1sYHtm/rfVoWv6xJoAAADJSURBVA7FJW007smJiKuA1UAHcEdmro2IG4C+zOwFbqcxzdtPY2ZnZesSqwpK1s1NwDTgt8UeF89n5vKWhVbLlawb6S1K1s1q4JMRsQ7YB3wjM7e2LrVarWTdfB34eUR8lcaypCv8g+7hLSJ+Q2NpbGdxb9e3gSMAMvOnNO71ugjoB3YCX2hN0vLCmpYkSZJUVy5pkyRJklRbNjySJEmSasuGR5IkSVJt2fBIkiRJqi0bHkmSJEm1ZcMjSZIkqbZseCRJkiTV1v8BKMl5yk/81+oAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
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},
{
"data": {
"text/plain": [
"array([0. , 0. , 0.00058348, 0.00125031, 0.00250063,\n",
" 0.00475119, 0.00675169, 0.00916896, 0.012003 , 0.01508711,\n",
" 0.01850463, 0.02242227, 0.02583979, 0.02925731, 0.03367509,\n",
" 0.03700925, 0.04101025, 0.04467784, 0.04867884, 0.05326332,\n",
" 0.05776444, 0.06234892, 0.06726682, 0.07151788, 0.07551888,\n",
" 0.07951988, 0.08360423, 0.08877219, 0.09218971, 0.09569059,\n",
" 0.09960824, 0.10394265, 0.1089439 , 0.11327832, 0.11736267,\n",
" 0.11961324, 0.12244728, 0.12511461, 0.12836542, 0.13119947,\n",
" 0.13453363, 0.13770109, 0.14011836, 0.14486955, 0.14695341,\n",
" 0.1498708 , 0.15312161, 0.15553888, 0.15837293, 0.16104026,\n",
" 0.16395766, 0.16662499, 0.16937568, 0.17262649, 0.17512712,\n",
" 0.17671084, 0.17821122, 0.18046178, 0.18321247, 0.18471284,\n",
" 0.18621322, 0.18846378, 0.18938068, 0.19104776, 0.19238143,\n",
" 0.19404851, 0.19554889, 0.1967992 , 0.1978828 , 0.19913312,\n",
" 0.1998833 , 0.20130033, 0.20246728, 0.20305076, 0.20446778,\n",
" 0.20555139, 0.20705176, 0.20805201, 0.20913562, 0.20996916,\n",
" 0.2108027 , 0.2117196 , 0.21221972, 0.21255314, 0.21288655,\n",
" 0.21330333, 0.21347003, 0.21380345, 0.21447028, 0.2148037 ,\n",
" 0.21513712, 0.21563724, 0.21613737, 0.21638743, 0.21647078,\n",
" 0.21647078, 0.21647078, 0.21647078, 0.21647078, 0.21647078])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
"x_vals = np.linspace(0, 1, 100)\n",
"y_vals = ep(x_vals, test_labeled, 'result_Y', 'X', logreg)\n",
"plt.figure(figsize=(14, 8))\n",
"plt.plot(x_vals, y_vals)\n",
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### On COMPAS data\n",
"\n",
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"#### Predictive models\n",
"\n",
"Let's build the predictive models (first here random forest and logistic regression). Some of our variables are string so they will first have to be transformed to be dummy / indicator variables."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# convert string values to dummies, drop first so full rank\n",
"compas_dummy = pd.get_dummies(compas, columns=['c_charge_degree', 'race', 'age_cat', 'score_text', 'sex'], drop_first=True)\n",
"\n",
"########\n",
"\n",
"predict_columns = ['priors_count', 'days_b_screening_arrest', 'length_of_stay',\n",
" 'c_charge_degree_M', 'race_Asian', 'race_Caucasian', 'race_Hispanic',\n",
" 'race_Native American', 'race_Other', 'age_cat_Greater than 45',\n",
" 'age_cat_Less than 25', 'score_text_Low', 'score_text_Medium', 'sex_Male']\n",
"\n",
"response_column = 'two_year_recid'\n",
"\n",
"# instantiate the model (using the default parameters)\n",
"logreg_c = LogisticRegression(solver='lbfgs', max_iter=1000)\n",
"\n",
"# fit, reshape X to be of shape (n_samples, n_features)\n",
"logreg_c.fit(compas_dummy[predict_columns], compas_dummy[response_column])\n",
"\n",
"# predict probabilities and attach to data\n",
"#label_probs_logreg = logreg_c.predict_proba(test.X.values.reshape(-1, 1))\n",
"#test = test.assign(B_prob_0_machine=label_probs_logreg[:, 0])\n",
"\n",
"########\n",
"\n",
"# instantiate the model\n",
"forest_c = RandomForestClassifier(n_estimators=300, max_depth=5, random_state=0)\n",
"\n",
"# fit, reshape X to be of shape (n_samples, n_features)\n",
"forest_c = forest.fit(compas_dummy[predict_columns], compas_dummy[response_column])\n",
"\n",
"# predict probabilities and attach to data\n",
"#label_probs_forest = forest.predict_proba(test.X.values.reshape(-1, 1))\n",
"#test = test.assign(B_prob_0_forest=label_probs_forest[:, 0])"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"failures_compas = np.zeros((11, 2))\n",
"\n",
"for r in np.arange(0, 11):\n",
" ## Causal model with logistic regression\n",
" failures_compas[r, 0] = ep([r / 10], compas_dummy, response_column, predict_columns, logreg_c)\n",
" \n",
" ## Causal model with random forest classifier\n",
" failures_compas[r, 1] = ep([r / 10], compas_dummy, response_column, predict_columns, forest_c)\n",
"\n",
"# klassifikaatioille scipy.stats semin kautta error barit xerr ja yerr argumenttien kautta\n",
"\n",
"plt.figure(figsize=(14, 8))\n",
"plt.plot(np.arange(0, 11) / 10, failures_compas[:, 0], label='Causal model, log.')\n",
"plt.plot(np.arange(0, 11) / 10, failures_compas[:, 1], label='Causal model, for.')\n",
"\n",
"plt.title('Failure rate vs. Acceptance rate - COMPAS')\n",
"plt.xlabel('Leniency')\n",
"plt.ylabel('Empirical performance')\n",
"plt.legend()\n",
"plt.show()\n"
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
"number_sections": true,
"sideBar": true,
"skip_h1_title": true,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": true,
"toc_position": {
"height": "calc(100% - 180px)",
"left": "10px",
"top": "150px",
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},
"varInspector": {
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